This disclosure relates generally to computer based safety mechanisms. More particularly, it relates to teaching a machine learning system to recognize and ameliorate risk factors relevant to driving safety.
Driver inattentiveness is a major factor in vehicle accidents. It can be caused by many reasons including driver conditions such as sleepiness, sickness, anxiety, stress, distraction and so forth.
The prior art has developed multiple techniques to monitor driver attentiveness and concentration on the driving task. Some of these techniques are mechanical, e.g., relying on a set of grip pressure sensors; when the pressure on any one sensor falls below a predetermined value, an alarm is activated. Sensors on wearable devices can discern the driver is engaged in secondary activities, which may indicate the driver is not satisfactorily involved in the driving process. Other techniques include acquiring driver gaze distribution information to assess the driver's attention on the driving task. For example, when the driver's gaze is lateral, directed to the vehicle interior, rather than on the road or vehicle meter, it is an indication that the driver is inattentive.
In such systems, it is known to use an audible alarm or visual alarm to gain the driver's attention. However, many of the system alarms are repetitive and serve to annoy the driver which may further decrease the driver's attentiveness on the driving task. The repetition can cause some drivers to ignore the alarms and other drivers to disengage the safety feature to eliminate the aggravation.
While a number of computer aided mechanisms for improving driver safety have been proposed in the art, further improvements in such computer aided mechanisms are needed.